There are numerous instances when businesses or other entities need to accurately match a customer list against other types of lists, such as consolidation and cleansing of multiple lists that may have been generated by various different business functions. Consolidation, cleansing and removing duplicates in address lists is often required for a mailer to receive postal discounts. Additionally, as more businesses wish to compete for customers in the global marketplace, they will want to obtain new data about their customers to broaden their customer understanding, to use in data analysis, and to craft marketing campaigns. Such data is only useful when it is accurately matched to the right customers.
Traditional address matching applications use information such as a business name and physical address. A positive match is identified only if both of the business name and physical address are similar. If either one of these is not similar enough, existing applications will return a “no match” result. In recent years, e-mail addresses have also become available for use along with the physical address for matching, especially for records that may not include a business name. The use of e-mail addresses suffers from the same drawbacks as described above, in that current applications do not take full advantage of the information embedded in e-mail addresses and use it as-is along with other fields to do address and name matching. Thus, only an exact match on an e-mail address domain will return a positive match. This only partially improves matching accuracy and in some cases can actually cause more false positive matches. For example, in the situation of a multi-office building with multiple small businesses, these small businesses may not have their own registered email domains. They will often use Internet Service Provider (ISP), e.g., optonline.net, or E-mail Service Provider (ESP), e.g., yahoo.com, provided e-mail addresses and domains. Using these domains as an alternate matching field would create an unpredictable number of false positive matches, as all of the businesses at that physical location with the same domain will result in an erroneous match to each other.